Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm

Tran, Minh-Trieu; Kim, Soo-Hyung; Yang, Hyung-Jeong; Lee, Guee-Sang; Oh, Il‐Seok; Kang, Sae-Ryung

doi:10.3390/s21134556

Cited by 8 publications

(5 citation statements)

References 59 publications

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“…Moreover, refs. [43,44] integrate attention modules in their deep neural network to solve segmentation tasks such as esophagus and lungs in medical images. Motivated by channel attention, we improved the localization mask by adopting an attention mechanism to focus on the important channels.…”

Section: Attention Mechanismmentioning

confidence: 99%

Weakly Supervised Learning for Transmission Line Detection Using Unpaired Image-to-Image Translation

Choi

Lee

2022

Remote Sensing

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To achieve full autonomy of unmanned aerial vehicles (UAVs), obstacle detection and avoidance are indispensable parts of visual recognition systems. In particular, detecting transmission lines is an important topic due to the potential risk of accidents while operating at low altitude. Even though many studies have been conducted to detect transmission lines, there still remains many challenges due to their thin shapes in diverse backgrounds. Moreover, most previous methods require a significant level of human involvement to generate pixel-level ground truth data. In this paper, we propose a transmission line detection algorithm based on weakly supervised learning and unpaired image-to-image translation. The proposed algorithm only requires image-level labels, and a novel attention module, which is called parallel dilated attention (PDA), improves the detection accuracy by recalibrating channel importance based on the information from various receptive fields. Finally, we construct a refinement network based on unpaired image-to-image translation in order that the prediction map is guided to detect line-shaped objects. The proposed algorithm outperforms the state-of-the-art method by 2.74% in terms of F1-score, and experimental results demonstrate that the proposed method is effective for detecting transmission lines in both quantitative and qualitative aspects.

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Section: Attention Mechanismmentioning

confidence: 99%

Weakly Supervised Learning for Transmission Line Detection Using Unpaired Image-to-Image Translation

Choi

Lee

2022

Remote Sensing

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“…Huang et al [ 33 ] proposed channel attention U-Net to segment esophageal cancer with a higher Dice value. Tran et al [ 34 ] proposed a novel U-Net with an attention mechanism combined and STA-PLEalgorithm to achieve esophagus segmentation using 3D images. An overview comparison of the methods for esophageal lesion segmentation is shown in Table 2 .…”

Section: Related Workmentioning

confidence: 99%

Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention

Tang

Cheang

et al. 2021

Sensors

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The automatic analysis of endoscopic images to assist endoscopists in accurately identifying the types and locations of esophageal lesions remains a challenge. In this paper, we propose a novel multi-task deep learning model for automatic diagnosis, which does not simply replace the role of endoscopists in decision making, because endoscopists are expected to correct the false results predicted by the diagnosis system if more supporting information is provided. In order to help endoscopists improve the diagnosis accuracy in identifying the types of lesions, an image retrieval module is added in the classification task to provide an additional confidence level of the predicted types of esophageal lesions. In addition, a mutual attention module is added in the segmentation task to improve its performance in determining the locations of esophageal lesions. The proposed model is evaluated and compared with other deep learning models using a dataset of 1003 endoscopic images, including 290 esophageal cancer, 473 esophagitis, and 240 normal. The experimental results show the promising performance of our model with a high accuracy of 96.76% for the classification and a Dice coefficient of 82.47% for the segmentation. Consequently, the proposed multi-task deep learning model can be an effective tool to help endoscopists in judging esophageal lesions.

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“…However, few studies have explored the effect of clinical segmentation variability on algorithm training, and defined methods to quantify and identify meaningful variation at the individual clinician level. Most of the work to date regarding esophageal segmentation has focused on improving the overall performance of segmentation models by proposing innovative model architectures and evaluating methods using a limited number of test cases 13 , 14 . Their training data were acquired directly from the clinic without standardization in terms of segmentation inconsistency 13 – 16 .…”

Section: Introductionmentioning

confidence: 99%

Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus

Yan,

Kehayias,

et al. 2024

Sci Rep

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Manual segmentation of tumors and organs-at-risk (OAR) in 3D imaging for radiation-therapy planning is time-consuming and subject to variation between different observers. Artificial intelligence (AI) can assist with segmentation, but challenges exist in ensuring high-quality segmentation, especially for small, variable structures, such as the esophagus. We investigated the effect of variation in segmentation quality and style of physicians for training deep-learning models for esophagus segmentation and proposed a new metric, edge roughness, for evaluating/quantifying slice-to-slice inconsistency. This study includes a real-world cohort of 394 patients who each received radiation therapy (mainly for lung cancer). Segmentation of the esophagus was performed by 8 physicians as part of routine clinical care. We evaluated manual segmentation by comparing the length and edge roughness of segmentations among physicians to analyze inconsistencies. We trained eight multiple- and individual-physician segmentation models in total, based on U-Net architectures and residual backbones. We used the volumetric Dice coefficient to measure the performance for each model. We proposed a metric, edge roughness, to quantify the shift of segmentation among adjacent slices by calculating the curvature of edges of the 2D sagittal- and coronal-view projections. The auto-segmentation model trained on multiple physicians (MD1-7) achieved the highest mean Dice of 73.7 ± 14.8%. The individual-physician model (MD7) with the highest edge roughness (mean ± SD: 0.106 ± 0.016) demonstrated significantly lower volumetric Dice for test cases compared with other individual models (MD7: 58.5 ± 15.8%, MD6: 67.1 ± 16.8%, p < 0.001). A multiple-physician model trained after removing the MD7 data resulted in fewer outliers (e.g., Dice ≤ 40%: 4 cases for MD1-6, 7 cases for MD1-7, Ntotal = 394). While we initially detected this pattern in a single clinician, we validated the edge roughness metric across the entire dataset. The model trained with the lowest-quantile edge roughness (MDER-Q1, Ntrain = 62) achieved significantly higher Dice (Ntest = 270) than the model trained with the highest-quantile ones (MDER-Q4, Ntrain = 62) (MDER-Q1: 67.8 ± 14.8%, MDER-Q4: 62.8 ± 15.7%, p < 0.001). This study demonstrates that there is significant variation in style and quality in manual segmentations in clinical care, and that training AI auto-segmentation algorithms from real-world, clinical datasets may result in unexpectedly under-performing algorithms with the inclusion of outliers. Importantly, this study provides a novel evaluation metric, edge roughness, to quantify physician variation in segmentation which will allow developers to filter clinical training data to optimize model performance.

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Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm

Cited by 8 publications

References 59 publications

Weakly Supervised Learning for Transmission Line Detection Using Unpaired Image-to-Image Translation

Weakly Supervised Learning for Transmission Line Detection Using Unpaired Image-to-Image Translation

Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention

Edge roughness quantifies impact of physician variation on training and performance of deep learning auto-segmentation models for the esophagus

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